Python Project Task A - AI Document Management System

Overview

This is a Flask-based AI-powered document management and onboarding system that helps organizations manage their internal documents and provide intelligent assistance to users through AI integration.

Features

Core Functionality

• Document Management: Upload, store, and organize various document types (PDF, DOCX, MD, TXT)
• AI Integration: Powered by Google Gemini AI for intelligent document analysis and user assistance
• User Management: Secure user authentication and session management
• Knowledge Base: Enhanced knowledge base with document indexing and retrieval
• File Processing: Automatic file processing and content extraction
• Web Interface: User-friendly web interface with dashboard and file management

AI Capabilities

• Document content analysis
• Intelligent Q&A based on document corpus
• Context-aware responses
• Multi-language support (including Azerbaijani)

Security Features

• User authentication system
• Secure file upload handling
• Session management
• SQL injection protection

Project Structure

├── app.py                 # Main Flask application
├── config.py             # Configuration settings
├── models.py             # Core models (KnowledgeBase, UserManager, AIAssistant)
├── file_manager.py       # File handling and processing
├── requirements.txt      # Python dependencies
├── setup.py             # Package setup configuration
├── .gitignore           # Git ignore rules
├── documents/           # Uploaded and processed documents
├── templates/           # HTML templates
│   ├── dashboard.html   # Main dashboard
│   ├── files.html       # File management interface
│   └── login.html       # Login page
├── test_documents/      # Sample test documents
├── simple_test_docs/    # Simple test documents
└── tests/               # Test files
    ├── simple_test.py
    ├── test_ai_integration.py
    └── test_large_files.py

Installation

Prerequisites

• Python 3.8 or higher
• Google Gemini API key

Setup Instructions

1. Clone the repository

   git clone <your-repo-url>
   cd Python_Project_Task_A

2. Create virtual environment

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Environment Configuration Create a .env file in the root directory:

   GEMINI_API_KEY=your_google_gemini_api_key_here
   FLASK_SECRET_KEY=your_secret_key_here
   FLASK_ENV=development
   

5. Initialize the database

   python -c "from models import UserManager; UserManager().create_tables()"

6. Run the application

   python app.py

The application will be available at http://localhost:5000

Configuration

Environment Variables

• GEMINI_API_KEY: Your Google Gemini API key
• FLASK_SECRET_KEY: Secret key for Flask sessions
• FLASK_ENV: Environment mode (development/production)

File Upload Settings

• Maximum file size: 16MB
• Supported formats: PDF, DOCX, MD, TXT
• Upload directory: documents/

Usage

Web Interface

1. Login: Access the system through the login page
2. Dashboard: View system overview and statistics
3. File Management: Upload, view, and manage documents
4. AI Assistant: Ask questions about your documents

API Endpoints

• GET /: Dashboard (login required)
• GET /login: User login page
• POST /login: Process login
• GET /logout: User logout
• GET /files: File management interface
• POST /upload: Upload new files
• POST /ask: AI assistant Q&A

Testing

Run the test suite:

# Simple functionality test
python simple_test.py

# AI integration test
python test_ai_integration.py

# Large file handling test
python test_large_files.py

Dependencies

Core Dependencies

• Flask 3.0.0: Web framework
• google-generativeai 0.3.2: Google Gemini AI integration
• Werkzeug 3.0.1: WSGI utilities

Production Dependencies

• gunicorn 21.2.0: Production WSGI server
• python-dotenv 1.0.0: Environment variable management

Database

The application uses SQLite databases:

• users.db: User authentication data
• file_index.db: Document indexing and metadata

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or support, please contact the development team.

Changelog

Version 1.0.0

• Initial release
• Basic document management
• AI integration with Google Gemini
• User authentication system
• Web interface implementation